Introduction
# A tibble: 779 × 6
track.name tempo loudn…¹ energy tempo2 tempo3
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Colors - Radio Edit 150. -3.40 0.937 150. 150.
2 Lessons In Love - Headhunterz Remix Radio… 150. -3.86 0.893 150. 150.
3 Never Say Goodbye - Wildstylez Radio Edit 148. -3.41 0.834 148. 148.
4 Year Of Summer - Radio Edit 150. -5.57 0.888 150. 150.
5 Catch Me (feat. Naaz) 145. -3.33 0.916 145. 145.
6 Rockstar (feat. DV8) 128. -5.75 0.973 128. 166.
7 Our Church 150. -3.47 0.957 150. 150.
8 Destiny - Edit 150. -4.63 0.791 150. 150.
9 Home 150. -3.81 0.896 150. 150.
10 Waiting For You - Rebourne Remix 75.0 -3.34 0.974 150. 150.
# … with 769 more rows, and abbreviated variable name ¹loudnessIn this storyboard I will attempt to explain the differences between Hardstyle and Hardcore. The data I will use for this project is my own playlist called “Kasper Hardstyle/core”. In this playlist you can find a combination of the two mentioned genres. There are also some songs which could be categorized in the genre Frenchcore but the percentage of songs that could be identified with that genre is very small in my playlist. The reason I chose this subject is because I am very passionate about these genres. Especially since in my spare time I produce songs in these genres. I will try to create a clear view of the differences of these genres and the first difference I’d like to highlight is the difference in tempo. Hardstyle usually has a bpm ranging from 140 to 165, while Hardcore is a bit quicker, most songs are between the 180 and 220 songs. The differences in bpm in both genres can be explained because of the many subgenres that both genres have. The more melodic or “euphoric” subgenres tend to be slower and less melodic subgenres have faster tempo’s.
In the plot you can see tempo on the x-axis with loudness on the y-axis the tempo, the colour of the data points are determined by energy of the tracks. With this plot I’m trying to see if you can clearly see a difference in the genres when it comes to loudness and if you can really separate the two by just tempo. Both these things are the case. The line in the plot is slowly rising as the bpm increases which means that the tracks are gaining loudness on average. The reason this is interesting is because loudness in the case of these songs can also be interpreted as harshness. The songs that are all the way on the right are normally regarded as very rough and harsh songs. The other fun thing is that around the 160 bpm you can very clearly see that the songs switch from genre.
Unexpected chromagram result
The song I selected for analyzing the chroma is “Warriors 2022 edit” by DRS. The reason I selected this song is because I expected it to have very interesting results since it has sections with only one sound playing. These sounds are in the case of Hardcore and Hardstyle nearly always the kicks. In this song you can see some very clear brightness between 5 seconds and 60 seconds. These bright spots are the vocals of the song. That is the way to recognize the introduction. I expected to see more clear sections in the chromagram, but after listening to the song again while analyzing the chromagram I realized why the sections flow more nicely into eachother than I expected. The reason is that the genres I’m covering in this storyboard tend to use multiple choruses that flow into each other. But if you look closely you can see where the choruses are. An example would be to look at the timeframe 100 seconds to roughly 110 second. In that time you see a brighter bar in the E and F notes. This is because those are the two notes that the kick uses for that duration. So that is one of the sections of the song. By doing the same and looking for bars of lighter colours you can spot the sections of the song. This however is far from what I expected since I expected the chromagram to be emptier with more bright bars at the choruses.
Timbre
